Algorithms might be everywhere, but like us, they’re deeply flawed

What’s the problem?

As algorithms become entrenched into society, the debate about their effects rages on.

In essence, algorithms are sequences of instructions used to solve problems and perform functions in computer programming. As mathematical expressions, algorithms existed long before modern computers. While they vary in application, all algorithms have three things in common: clearly-defined beginning and ending points, discrete sets of “steps,” and design meant to address a specific type of problem.

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Algorithms are … (screenshot)

Algorithms, in a sense, are the “nervous system” of AI. They are the models that underpin machine learning, prediction, and problem solving. Yet, as many researchers argue, due to their design by humans, algorithms can never be neutral.

“We need to remember that [AI systems] are made out of software. And we don’t know how to write perfect software … the consequence is that however much we might benefit from these devices …, they may not work exactly the way they were intended to work or the way we expect them to. And the more we rely on [AI systems], the more surprised we may be when they don’t work the way we expect.”

“The way we expect” is key here, because algorithms are a computer-simulated reflection of encoded human expectations.

Engineering memories

The more we, as humans, rely on algorithms, the more our reality becomes encoded with other people’s flawed expectations. As more AI-powered systems come online, this type of calculated bias will permeate every level of our lives — even our memories and past experiences.

Biased learning, troubled future?

As algorithms “learn” more about us through our financial data, location history, biometric features, voice patterns, social networks, stored memories, and “smart home” devices, we move towards a reality constructed by imperfect machine learning systems which try to understand us through other people’s expectations and sets of “rules”.

Does this mean that we live inside a computer simulation? I’ll defer that question to Elon Musk, who has said, “there’s a billion to one chance we’re living in base reality”. Cerf, however, warns that it’s a mistake to “imbue artificial intelligences with a breadth of knowledge that they don’t actually have, and also with social intelligence that they don’t have”.

The algorithmic end game, AI, will get better with time, but it will always be flawed. Even in straightforward applications like a game of chess, algorithms can leave people clueless as to how they arrived at a certain outcome.

Great expectations

Cerf talked about a scenario in which IBM’s “Deep Blue” supercomputer, playing world chess champion Gary Kasparov, made a move that Kasparov could not understand.

IBM’s Deep Blue by James the photographer

I mean, it made no sense whatsoever. And he was clearly concerned about it, because he thought for quite a long time and had to play the endgame much faster … in the end it turned out it was a bug.

It was just a mistake. The computer didn’t know what it was doing. But Kasparov assumed that it did, and lost the game as a result.

Bad or good?

Is the social use of algorithms inherently “bad,” provided they form the basis of “intelligence” in AI?“ David Lazer, a computer scientist at Northeastern University, is sceptical. In a recent Science article he said:

The fact that human lives are regulated by code is hardly a new phenomenon. Organizations run on their own algorithms, called standard operating procedures. And anyone who has been told that “it’s a rule” knows that social rules can be as automatic and thoughtless as any algorithm.

It’s a little unnerving to think that we’re building machines that we don’t understand … Not only in the technical sense, like what’s it going to do or how is it going to behave, but also in the social sense, how is it going to impact our society?

Just like us

Bias — made with Error Message Generator (CC 3.0)

So, algorithms, the underlying process of decision making in artificial intelligence systems are imperfect, prone to bias, and make unpredictable decisions that impact the future.